File size: 6,878 Bytes
2d4d351 55a8b23 2d4d351 55a8b23 2d4d351 55a8b23 2d4d351 55a8b23 2d4d351 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
datasets:
- essays_su_g
metrics:
- accuracy
model-index:
- name: longformer-simple
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: essays_su_g
type: essays_su_g
config: simple
split: train[80%:100%]
args: simple
metrics:
- name: Accuracy
type: accuracy
value: 0.8299721206415873
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# longformer-simple
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4321
- Claim: {'precision': 0.5835557928457021, 'recall': 0.52447216890595, 'f1-score': 0.5524387161991408, 'support': 4168.0}
- Majorclaim: {'precision': 0.6944444444444444, 'recall': 0.824814126394052, 'f1-score': 0.754035683942226, 'support': 2152.0}
- O: {'precision': 0.934596507248031, 'recall': 0.8874918707999133, 'f1-score': 0.9104353143937287, 'support': 9226.0}
- Premise: {'precision': 0.8580758203249442, 'recall': 0.8924045390540877, 'f1-score': 0.8749035689634171, 'support': 12073.0}
- Accuracy: 0.8300
- Macro avg: {'precision': 0.7676681412157805, 'recall': 0.7822956762885007, 'f1-score': 0.7729533208746281, 'support': 27619.0}
- Weighted avg: {'precision': 0.8294594932357695, 'recall': 0.8299721206415873, 'f1-score': 0.8286917107662684, 'support': 27619.0}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Claim | Majorclaim | O | Premise | Accuracy | Macro avg | Weighted avg |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log | 1.0 | 41 | 0.6033 | {'precision': 0.4527056753189617, 'recall': 0.2468809980806142, 'f1-score': 0.31951560316721006, 'support': 4168.0} | {'precision': 0.5835601524224279, 'recall': 0.49814126394052044, 'f1-score': 0.5374780646778641, 'support': 2152.0} | {'precision': 0.8875888965359028, 'recall': 0.8387166702796445, 'f1-score': 0.862460989745876, 'support': 9226.0} | {'precision': 0.7685754850922859, 'recall': 0.9416052348215025, 'f1-score': 0.8463371054198927, 'support': 12073.0} | 0.7678 | {'precision': 0.6731075523423946, 'recall': 0.6313360417805705, 'f1-score': 0.6414479407527107, 'support': 27619.0} | {'precision': 0.7462473548536118, 'recall': 0.7678409790361708, 'f1-score': 0.7481547773024915, 'support': 27619.0} |
| No log | 2.0 | 82 | 0.4684 | {'precision': 0.5774099318403116, 'recall': 0.42682341650671785, 'f1-score': 0.49082632087184436, 'support': 4168.0} | {'precision': 0.6601866251944012, 'recall': 0.7890334572490706, 'f1-score': 0.7188823031329382, 'support': 2152.0} | {'precision': 0.9429934406678593, 'recall': 0.8570344678083677, 'f1-score': 0.8979615013343932, 'support': 9226.0} | {'precision': 0.8198954421618437, 'recall': 0.9223059720036445, 'f1-score': 0.8680907460824822, 'support': 12073.0} | 0.8153 | {'precision': 0.7501213599661041, 'recall': 0.7487993283919501, 'f1-score': 0.7439402178554144, 'support': 27619.0} | {'precision': 0.8119780357779204, 'recall': 0.815344509214671, 'f1-score': 0.809509801603999, 'support': 27619.0} |
| No log | 3.0 | 123 | 0.4395 | {'precision': 0.5962599632127529, 'recall': 0.4666506717850288, 'f1-score': 0.5235531628532973, 'support': 4168.0} | {'precision': 0.7146464646464646, 'recall': 0.7890334572490706, 'f1-score': 0.75, 'support': 2152.0} | {'precision': 0.9242167175658862, 'recall': 0.885649252113592, 'f1-score': 0.9045220567886201, 'support': 9226.0} | {'precision': 0.8378995433789954, 'recall': 0.9119522902344074, 'f1-score': 0.873358981477809, 'support': 12073.0} | 0.8264 | {'precision': 0.7682556722010248, 'recall': 0.7633214178455248, 'f1-score': 0.7628585502799315, 'support': 27619.0} | {'precision': 0.820663866978074, 'recall': 0.8263876317028133, 'f1-score': 0.8213676477094007, 'support': 27619.0} |
| No log | 4.0 | 164 | 0.4321 | {'precision': 0.5835557928457021, 'recall': 0.52447216890595, 'f1-score': 0.5524387161991408, 'support': 4168.0} | {'precision': 0.6944444444444444, 'recall': 0.824814126394052, 'f1-score': 0.754035683942226, 'support': 2152.0} | {'precision': 0.934596507248031, 'recall': 0.8874918707999133, 'f1-score': 0.9104353143937287, 'support': 9226.0} | {'precision': 0.8580758203249442, 'recall': 0.8924045390540877, 'f1-score': 0.8749035689634171, 'support': 12073.0} | 0.8300 | {'precision': 0.7676681412157805, 'recall': 0.7822956762885007, 'f1-score': 0.7729533208746281, 'support': 27619.0} | {'precision': 0.8294594932357695, 'recall': 0.8299721206415873, 'f1-score': 0.8286917107662684, 'support': 27619.0} |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|